Python & AI /ML/ Deep Learning training Sept 14-15 and Sept 28-29 2019

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215 Fourier Ave #140, Fremont, CA 94539

iBridge

Warm Springs, CA 94539

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Erudition Inc. is offering Python for Data Science and Machine Learning/AI/ Deep Learning training Sept 14-15 and Sept 28-29 2019.

About this Event

2 Day Python Deep Dive and Python for Data Science bootcamp

Erudition Inc. is offering Python for Data Science training on Sept 14-15 2019 and Machine Learning, Artificial Intelligence and Deep Learning Sept 28-29 2019.

Our mission: Erudition Inc.'s mission is to provide education in emerging technologies to masses at no cost or very affordable rate. What is life's objective at the end of the day? Life is fleeting, and permanence in this world is something we all strive for. The best way to achieve permanence is through sharing knowledge.

This course is taught by Ivy League and Stanford trained Artificial Intelligence, Data Science and Machine Learning professional. http://www.linkedin.com/in/mehtabhairav. Curriculum is revised every month with new content and latest industry standards.

Time Date :

1. Python for Data Science Sept 14-15 2019 9AM to 6PM

2. Machine Learning, Artificial Intelligence and Deep Learning Sept 28-29 2019 9AM to 6PM

Location: iBridge 215 Fourier Ave #140, Fremont, CA 94539

Link to Enroll: http://erudition.eventbrite.com

Instructor: Bhairav Mehta

Bhairav Mehta is Data Science Manager at Apple Inc. He has 15 years experience in Analytics and Data Science space at various fortune 100 companies. Bhairav Mehta is academician and tenured faculty at various Bay area Universities. Bhairav Mehta has taught 1000s of students in AI, ML and Big Data technologies over last 5 years. He also gives talks at Association of Computing Machinery (ACM), IEEE Computer Science society, Global Big Data and AI conferences, Open Data science conference and other forums. Bhairav Mehta has 5 graduate degrees from top institutes: MS Computer Science (GeorgiaTech), MBA (Cornell University), MS Statistics (Cornell University) etc. Bhairav recently completed very reputed and rigorous Stanford Artificial Intelligence Graduate Certificate. He will share the material and knowhow in the course.

Linkedin Profile: https://www.linkedin.com/in/mehtabhairav/­

Erudition Website: http://www.eruditionsiliconvalley.com

Bhairav Mehta talk videos and other conference proceedings: https://bit.ly/2MrMbGV­

What is this course about?

Python for Data Science: This course is comprehensive course in Python for Data Science. Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets. This course will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you’ll learn how to use:

python, jupyter notebooks, pandas, numpy, matplotlib, git and many other tools.

You will learn these tools all within the context of solving compelling data science problems.After completing this course, you’ll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.

By learning these skills, you’ll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings.

Schedule for Python for Data Science Course Sept 14-15 2019:

  1. IPython: Beyond Normal Python
  2. Help and Documentation in IPython
  3. Keyboard Shortcuts in the IPython Shell
  4. IPython Magic Commands
  5. Input and Output History
  6. IPython and Shell Commands
  7. Errors and Debugging
  8. Profiling and Timing Code
  9. More IPython Resources
  10. Version Control and git

NumPy

  1. Introduction to NumPy
  2. Understanding Data Types in Python
  3. The Basics of NumPy Arrays
  4. Computation on NumPy Arrays: Universal Functions
  5. Aggregations: Min, Max, and Everything In Between
  6. Computation on Arrays: Broadcasting
  7. Comparisons, Masks, and Boolean Logic
  8. Fancy Indexing
  9. Sorting Arrays
  10. Structured Data: NumPy's Structured Arrays

Data Manipulation with Pandas

  1. Data Manipulation with Pandas
  2. Introducing Pandas Objects
  3. Data Indexing and Selection
  4. Operating on Data in Pandas
  5. Handling Missing Data
  6. Hierarchical Indexing
  7. Combining Datasets: Concat and Append
  8. Combining Datasets: Merge and Join
  9. Aggregation and Grouping
  10. Pivot Tables
  11. Vectorized String Operations
  12. Working with Time Series
  13. High-Performance Pandas: eval() and query()
  14. Further Resources

Visualization and Matplotlib

  1. Visualization with Matplotlib
  2. Simple Line Plots
  3. Simple Scatter Plots
  4. Visualizing Errors
  5. Density and Contour Plots
  6. Histograms, Binnings, and Density
  7. Customizing Plot Legends
  8. Customizing Colorbars
  9. Multiple Subplots
  10. Text and Annotation
  11. Customizing Ticks
  12. Customizing Matplotlib: Configurations and Stylesheets
  13. Three-Dimensional Plotting in Matplotlib
  14. Geographic Data with Basemap
  15. Visualization with Seaborn
  16. Further Resources

Python Deep Dive

  • Iterators
  • Python Lists and Generators
  • Functions
  • Logic, Control Flow and Filtering
  • Loops
  • Default arguments, variable-length arguments and scope
  • Lambda functions and error-handling
  • Working with Data and Database
  • SQL
  • SQLite database
  • Connecting to database
  • Database to dataframes and dictionaries
  • Working with text and unstructured data
  • Delimiters
  • Regular Expressions
  • Webscrapping
  • Beautiful soup and Python
  • Natural Language processing and Text mining
  • Sentiment Analysis with Wordcloud
  • NLTK and Spacy Packages

Machine Learning

  • What Is Machine Learning?
  • Introducing Scikit-Learn
  • Hyperparameters and Model Validation
  • Feature Engineering
  • In Depth: Naive Bayes Classification
  • In Depth: Linear Regression
  • In-Depth: Support Vector Machines
  • In-Depth: Decision Trees and Random Forests
  • In Depth: Principal Component Analysis
  • In-Depth: Manifold Learning
  • In Depth: k-Means Clustering
  • In Depth: Gaussian Mixture Models
  • In-Depth: Kernel Density Estimation
  • Application: A Face Detection Pipeline
  • Further Machine Learning Resources
  • Each topic includes codes and explanation step-by-step

Schedule for Artificial Intelligence, Machine Learning and Deep Learning Course:

Day 1

  • Overview and Introduction
  • Introduction to Neural Networks
  • Current /Future Industry Trends
  • Your First Neural Network
  • Google Colab Cloud Platform Intro and Set up
  • ML introduction
  • Machine Learning Algorithms and examples of each. Unsupervised and Supervised algorithms
  • Machine Learning Applications and Usecases
  • Gradient Descent, Error function
  • Training Neural Network
  • Tensorflow, Keras, Theano, Lasagne, Torch, Caffe introduction
  • Tensorflow Labs
  • Tensorflow and Keras labs for simple classification and clustering. Supervised and unsupervised.
  • Regularization Intro
  • Neural Network Architecture and Hyper Parameter tuning
  • Convolution Neural Network
  • Labs with Tensorflow and CNN, CNN with Regularization
  • CNN in Tensorflow
  • Weight Initialization
  • Auto Encoders
  • Transfer Learning
  • ImageNet, LeNet, Alexnet, VGGNet, Inception, ResNet
  • Object Detection
  • Auto Encoder and Transfer learning labs
  • Image Segmentation
  • Face Detection
  • Image Classification
  • Labs with Keras and TensorFlow

Day 2

  • Advanced Object Detection methods: R-CNN, F R-CNN, YOLO, Mask R-CNN, Labs
  • Labs for Image Classification
  • Labs for Image Segmentation and Face detection
  • Recurrent Neural Network Intro (RNN)
  • Long Short term Memory (LSTM)
  • Motivation for learning RNN and LSTM
  • Simple RNN and LSTM labs for Time Series
  • Cloud based tools for doing object detection, image classification and applications of CNN
  • RNN-LSTM Labs continued
  • Natural Language Processing (NLP)
  • Work2Vec, Word Embedding, PCA and T-SNE for Word Embedding
  • NLP Labs
  • Sequence to Sequence LSTM Chatbots and LSTM based Text Generation
  • Generative Adverserial Networks
  • Reinforcement Learning

Review and Introduction to advanced concepts in Neural Networks e.g. Reinforcement Learning, Generative Adversarial Networks, Autonomous Driving car etc.

Thanks

Bhairav Mehta

Link to Enroll: http://erudition.eventbrite.com

URL: http://www.eruditionsiliconvalley.com

Phone# 4086608118

Email: eruditionbayarea@gmail.com

Date and Time

Location

215 Fourier Ave #140, Fremont, CA 94539

iBridge

Warm Springs, CA 94539

View Map

Refund Policy

Contact the organizer to request a refund.

Eventbrite's fee is nonrefundable.

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